OralOpioids: Harnessing R Programming and Data Science to Combat Opioid Misuse
Bibliographic record
Abstract
AimsThis study aims to introduce the OralOpioids R package, a novel research tool for the in-depth study and analysis of opioid prescriptions in Canada, which reports a significant per-capita pharmaceutical opioid consumption.MethodsThe OralOpioids R package employs data from Health Canada's Drug Product Database (DPD), focusing on authorized oral opioids. It systematically filters drug identification numbers (DINs) by narcotic schedules and administration routes. Moreover, it calculates the morphine equivalent dose (MED) for each DIN using the CDC table. Core functions include MED calculation for specific drugs, brand name retrieval, opioid content extraction, and unit computations based on Canadian MED guidelines.ResultsWhen juxtaposed against renowned opioid calculators such as MDCalc, Oregon Pain, and Ohio Pain, the OralOpioids package exhibited a near-perfect correlation, with R-squared values consistently at 0.99.ConclusionsThe OralOpioids package, distinctively tailored for research, marks a significant stride in understanding and monitoring Canada's opioid milieu. By encompassing data on discontinued opioids, it fosters a nuanced comprehension of the opioid panorama, enabling historical insight and post-marketing watchfulness. Primarily targeting researchers, its scope extends to healthcare providers, insurers, and administrative boards, all of whom can leverage its potent capabilities for informed decision-making. Although currently centered on Canadian opioids, its flexible design is primed for future expansion, potentially capturing a global audience and catalyzing efforts against the opioid crisis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".